Streaming breakpoint for data tuples that stay in an operator graph too long

ABSTRACT

A streams manager monitors data tuples processed by a streaming application represented by an operator graph. The streams manager includes a tuple breakpoint mechanism that allows defining a tuple breakpoint that fires when a tuple has been in the operator graph too long. What constitutes too long can be defined in a number of different ways, including a time limit, a processing limit for multiple operators, and a processing limit for an individual operator. When the tuple breakpoint fires, one or more operators in the operator graph are halted according to specified halt criteria. Information corresponding to the breakpoint that fired is then displayed. The tuple breakpoint mechanism thus provides a way to debug a streaming application that may have data tuples that stay in the operator graph too long.

BACKGROUND

1. Technical Field

This disclosure generally relates to streaming applications, and morespecifically relates to enhancing debugging of a streaming applicationusing breakpoints for data tuples that stay in an operator graph toolong.

2. Background Art

Streaming applications are known in the art, and typically includemultiple operators coupled together in an operator graph that processstreaming data in near real-time. An operator typically takes instreaming data in the form of data tuples, operates on the data tuplesin some fashion, and outputs the processed data tuples to the nextoperator. Streaming applications are becoming more common due to thehigh performance that can be achieved from near real-time processing ofstreaming data.

Many streaming applications require significant computer resources, suchas processors and memory, to provide the desired near real-timeprocessing of data. However, the workload of a streaming application canvary greatly over time. Allocating on a permanent basis computerresources to a streaming application that would assure the streamingapplication would always function as desired (i.e., during peak demand)would mean many of those resources would sit idle when the streamingapplication is processing a workload significantly less than itsmaximum. Furthermore, what constitutes peak demand at one point in timecan be exceeded as the usage of the streaming application increases. Fora dedicated system that runs a streaming application, an increase indemand may require a corresponding increase in hardware resources tomeet that demand.

Cloud-based streaming is known in the art. Known systems for cloud-basedstreaming do not monitor data tuples to determine when a tuple has beenin an operator graph too long.

BRIEF SUMMARY

A streams manager monitors data tuples processed by a streamingapplication represented by an operator graph. The streams managerincludes a tuple breakpoint mechanism that allows defining a tuplebreakpoint that fires when a tuple has been in the operator graph toolong. What constitutes too long can be defined in a number of differentways, including a time limit, a processing limit for multiple operators,and a processing limit for an individual operator. When the tuplebreakpoint fires, one or more operators in the operator graph are haltedaccording to specified halt criteria. Information corresponding to thebreakpoint that fired is then displayed. The tuple breakpoint mechanismthus provides a way to debug a streaming application that may have datatuples that stay in the operator graph too long.

The foregoing and other features and advantages will be apparent fromthe following more particular description, as illustrated in theaccompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

The disclosure will be described in conjunction with the appendeddrawings, where like designations denote like elements, and:

FIG. 1 is a block diagram of a cloud computing node;

FIG. 2 is a block diagram of a cloud computing environment;

FIG. 3 is a block diagram of abstraction model layers;

FIG. 4 is a block diagram showing some features of a streams manager;

FIG. 5 is a table showing some suitable examples of breakpoint criteriafor a tuple breakpoint that define when a tuple has been in the operatorgraph too long;

FIG. 6 is a table showing some suitable examples of halt criteria thatcould be specified for a tuple breakpoint;

FIG. 7 is a table showing some suitable examples of information thatcould be displayed when a tuple breakpoint fires;

FIG. 8 is a flow diagram of a method for defining a tuple breakpoint;

FIG. 9 is a flow diagram of a method for processing a tuple breakpointwhen it fires;

FIG. 10 is a block diagram showing a specific example of an operatorgraph corresponding to a streaming application;

FIG. 11 is a table showing tuple breakpoint criteria for the specificexample in FIG. 10; and

FIG. 12 is a table showing halt criteria for the tuple breakpoint shownin FIG. 11.

DETAILED DESCRIPTION

The disclosure and claims herein relate to a streams manager thatmonitors data tuples processed by a streaming application represented byan operator graph. The streams manager includes a tuple breakpointmechanism that allows defining a tuple breakpoint that fires when atuple has been in the operator graph too long. What constitutes too longcan be defined in a number of different ways, including a time limit, aprocessing limit for multiple operators, and a processing limit for anindividual operator. When the tuple breakpoint fires, one or moreoperators in the operator graph are halted according to specified haltcriteria. Information corresponding to the breakpoint that fired is thendisplayed. The tuple breakpoint mechanism thus provides a way to debug astreaming application that may have data tuples that stay in theoperator graph too long.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forloadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a block diagram of an example of a cloudcomputing node is shown. Cloud computing node 100 is only one example ofa suitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 100 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 100 there is a computer system/server 110, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 110 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 110 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 110 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 110 in cloud computing node100 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 110 may include, but are notlimited to, one or more processors or processing units 120, a systemmemory 130, and a bus 122 that couples various system componentsincluding system memory 130 to processor 120.

Bus 122 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 110 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 110, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 130 can include computer system readable media in the formof volatile, such as random access memory (RAM) 134, and/or cache memory136. Computer system/server 110 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 140 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 122 by one or more datamedia interfaces. As will be further depicted and described below,memory 130 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions described in more detail below.

Program/utility 150, having a set (at least one) of program modules 152,may be stored in memory 130 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 152 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 110 may also communicate with one or moreexternal devices 190 such as a keyboard, a pointing device, a display180, a disk drive, etc.; one or more devices that enable a user tointeract with computer system/server 110; and/or any devices (e.g.,network card, modem, etc.) that enable computer system/server 110 tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interfaces 170. Still yet, computersystem/server 110 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter 160. Asdepicted, network adapter 160 communicates with the other components ofcomputer system/server 110 via bus 122. It should be understood thatalthough not shown, other hardware and/or software components could beused in conjunction with computer system/server 110. Examples, include,but are not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, dataarchival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 200 isdepicted. As shown, cloud computing environment 200 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 210A, desktop computer 210B, laptop computer210C, and/or automobile computer system 210N may communicate. Nodes 100may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 200 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 210A-Nshown in FIG. 2 are intended to be illustrative only and that computingnodes 100 and cloud computing environment 200 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 200 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and the disclosure andclaims are not limited thereto. As depicted, the following layers andcorresponding functions are provided.

Hardware and software layer 310 includes hardware and softwarecomponents. Examples of hardware components include mainframes 352; RISC(Reduced Instruction Set Computer) architecture based servers 354;servers 356; blade servers 358; storage devices 360; and networks andnetworking components 362. In some embodiments, software componentsinclude network application server software 364 and database software366.

Virtualization layer 320 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers368; virtual storage 370; virtual networks 372, including virtualprivate networks; virtual applications and operating systems 374; andvirtual clients 376.

In one example, management layer 330 may provide the functions describedbelow. Resource provisioning 378 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 380provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 382 provides access to the cloud computing environment forconsumers and system administrators. Service level management 384provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 386 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA. The management layer further includes astreams manager (SM) 350 as described herein. While the streams manager350 is shown in FIG. 3 to reside in the management layer 330, thestreams manager 350 actually may span other levels shown in FIG. 3 asneeded.

Workloads layer 340 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 386; software development and lifecycle management 390;virtual classroom education delivery 392; data analytics processing 394;transaction processing 396 and mobile desktop 398.

As will be appreciated by one skilled in the art, aspects of thisdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment (including firmware,resident software, micro-code, etc.) or an embodiment combining softwareand hardware aspects that may all generally be referred to herein as a“circuit,” “module” or “system.” Furthermore, aspects of the presentinvention may take the form of a computer program product embodied inone or more computer readable medium(s) having computer readable programcode embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a non-transitory computer readable storage medium. A computerreadable storage medium may be, for example, but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

FIG. 4 shows one suitable example of the streams manager 350 shown inFIG. 3. The streams manager 350 is software that manages one or morestreaming applications, including creating operators and data flowconnections between operators in an operator graph that represents astreaming application. The streams manager 360 includes a performancemonitor 410 and a tuple breakpoint mechanism 450. The performancemonitor 410 preferably includes a tuple monitor 420, one or morethresholds 430, and one or more historical logs 440. The tuple monitormonitors data tuples processed by operators in an operator graph, andlogs the monitored information in historical log(s) 440. The thresholds430 may include any suitable criteria, including criteria thatdetermines when a tuple breakpoint fires. Thus, in one implementation,the performance monitor 410 may compare the data in the historicallog(s) 440 to the threshold(s) 430 and may then signal the tuplebreakpoint mechanism 450 that the threshold(s) 430 have been satisfied,causing a tuple breakpoint to fire in the tuple breakpoint mechanism450. In an alternative implementation, the threshold(s) 430 in theperformance monitor are not related to breakpoint processing, with thetuple breakpoint mechanism 450 determining when a defined tuplebreakpoint 460 fires based on information in the historical log(s) 440of the performance monitor. The tuple breakpoint 460 preferablyspecifies at least one breakpoint criterion. Suitable examples ofbreakpoint criteria for tuple breakpoint 460 are shown in FIG. 5 toinclude a time limit for a tuple 510, a multiple operator processinglimit for a tuple 520, and an individual operator processing limit for atuple 530. A time limit for a tuple 510 preferably specifies a time thatis considered “too long” for a tuple to remain in the operator graph.This time could be specified from the time the tuple was created, orfrom the time the tuple was first operated on by an operator in theoperator graph. The multiple operator processing limit for a tuple 520specifies a threshold for a group of operators that process the tuple.For a relatively complex operator graph, this would allow operators tobe grouped to determine when all of the operators have processed a tuplea specified number of times. An individual operator processing limit fora tuple 530 preferably specifies a threshold number of times aparticular operator can process a tuple. Note that many different tuplebreakpoints could be active at the same time. Thus, three breakpointsthat specify 510, 520 and 530 could all be active at the same time. Inaddition, a single breakpoint could be defined with any suitablecombination of criteria. For example, a breakpoint could be defined tofire when a time limit for a tuple 510 is reached OR when an individualoperator processing limit for a tuple 530 is reached. In anotherexample, a breakpoint could be defined to fire when a time limit for atuple 510 is reached AND when an individual operator processing limitfor a tuple 530 is reached. The criteria in FIG. 5 for tuple breakpointare shown by way of example, and are not limiting. The disclosure andclaims herein expressly extend to any suitable criteria, includingcombinations of criteria, that can determine when a tuple breakpointfires. For the discussion herein the criteria used to define a tuplebreakpoint specify conditions under which it is determined the tuple hasbeen in the operator graph too long. As discussed above, this concept of“too long” can be defined in any suitable way using any suitablecriteria, combination of criteria, algorithm or heuristic.

Referring again to FIG. 4, the tuple breakpoint mechanism 450 includesan operator halt mechanism 470 that is used when a tuple breakpoint 460fires. The operator halt mechanism 470 defines at least one haltcriterion, shown as halt criteria 472 in FIG. 4, which specifies one ormore operators to halt when a breakpoint fires. Suitable examples ofhalt criteria 472 are shown in FIG. 6 to include halt all operators 610;halt a defined list of operators 620; halt an operator that caused abreakpoint 630; halt an operator that used the most amount of time 640;and halt an operator that executed the tuple the greatest number oftimes 650. Note that halting a defined list of operators 620 can includehalting all operators 610 when all operators are on the defined list. Inone suitable implementation, each defined tuple breakpoint 460 has itsown corresponding halt criteria 472. However, in a differentimplementation, halt criteria 472 could apply to all breakpoints, or toa specified subset of breakpoints.

The tuple breakpoint display mechanism 480 in FIG. 4 displays to a usertuple breakpoint display information 482. Examples of the tuplebreakpoint display information 482 are shown in FIG. 7 to include: whenthe tuple was created 710; the time the tuple has been in the operatorgraph 720; the number of operators that processed the tuple 730; and alist of operators that processed the tuple and how many times 740. Thisinformation can help a user debug a streaming application when datatuples are staying in an operator graph for too long.

Data tuples can stay in an operator graph too long when the operatorgraph includes at least one feedback path that routes data tuples to aprevious operator in the operator graph. However, in a simple linearoperator graph, there can also a threat of data tuples staying in theoperator graph too long. For example, if one of the operators is a joinoperator, a tuple could remain in the operator graph too long. Thedisclosure and claims herein apply to any operator graph where a tuplecan stay in the operator graph too long.

FIG. 8 shows a method 800 for defining a tuple breakpoint, such as tuplebreakpoint 460 shown in FIGS. 4 and 5. At least one breakpoint criterionis defined (step 810). At least one halt criterion is defined (step820). Tuple breakpoint display information is also defined (step 830).Method 800 is then done. With the tuple breakpoint defined as shown inmethod 800 in FIG. 8, the streaming application can be executed. Whenthe breakpoint criteria defined in step 810 is satisfied, the tuplebreakpoint fires.

Referring to FIG. 9, a method 900 shows what happens when a tuplebreakpoint fires. As long a no tuple breakpoint fires (step 910=NO),method 900 loops back and continues monitoring. When a tuple breakpointfires (step 910=YES), one or more operators in the operator graph arehalted according to the defined halt criteria (step 920), and the tuplebreakpoint display information is displayed (step 930). Method 900 isthen done.

A simple example is provided in FIGS. 10-12 to illustrate the conceptsdiscussed above. A simple operator graph 1000 is shown in FIG. 10 toinclude four operators, A, B, C and D. Operator A is a source of datatuples. Operator B processes data tuples received from Operator A andalso processes data tuples received from Operator C. Operator Cprocesses data tuples received from Operator B, and output data tupleseither to Operator D or to Operator B for further processing. In thissimple example, we assume Operator C includes logic to determine whethera tuple has been sufficiently processed or not. If a tuple has beensufficiently processed, the tuple is output to Operator D, which is asink for operators. If the tuple has not been sufficiently processed,the tuple is fed back to Operator B, which processes the tuple again.Because of this feedback path from operator C to Operator B, it ispossible for data tuples to remain in the operator graph 1000 too long.

Referring to FIG. 11, we assume for this simple example a tuplebreakpoint 1110 is defined that fires when operator C executes the sametuple more than 50 times 1120. Tuple breakpoint 1110 in FIG. 11 is onesuitable example for tuple breakpoint 460 shown in FIGS. 4 and 5. Thismeans a threshold of 50 is set for Operator C. On the 51st time thatOperator C executes the same tuple, as determined by the historicallog(s) 440 in the performance monitor 410, the tuple breakpoint 1110fires to indicate the tuple has been in the operator graph too long. Forthe simple example in FIGS. 10-12, we assume halt criteria 1210 in FIG.12 is defined that halts all operators 610. Thus, when the tuplebreakpoint 1110 fires on the 51st time that operator C processes thetuple, all operators, namely, A, B, C and D in the operator graph 1000,are halted. Once all the operators are halted, any suitable informationcan be displayed to a user, including any or all of the tuple breakpointdisplay info shown in FIG. 7. Of course, other information not shown inFIG. 7 could also be displayed to the user when a breakpoint fires.

The tuple breakpoint mechanism disclosed and claimed herein provides anincredibly powerful and flexible way to debug streaming applications bydetecting when data tuples have been in the operator graph too long.

The disclosure and claims herein relate to a streams manager thatmonitors data tuples processed by a streaming application represented byan operator graph. The streams manager includes a tuple breakpointmechanism that allows defining a tuple breakpoint that fires when atuple has been in the operator graph too long. What constitutes too longcan be defined in a number of different ways, including a time limit, aprocessing limit for multiple operators, and a processing limit for anindividual operator. When the tuple breakpoint fires, one or moreoperators in the operator graph are halted according to specified haltcriteria. Information corresponding to the breakpoint that fired is thendisplayed. The tuple breakpoint mechanism thus provides a way to debug astreaming application that may have data tuples that stay in theoperator graph too long.

One skilled in the art will appreciate that many variations are possiblewithin the scope of the claims. Thus, while the disclosure isparticularly shown and described above, it will be understood by thoseskilled in the art that these and other changes in form and details maybe made therein without departing from the spirit and scope of theclaims.

1-9. (canceled)
 10. A computer-implemented method executed by at leastone processor for debugging a streaming application, the methodcomprising: executing a streaming application that comprises an operatorgraph that includes a plurality of operators that process a plurality ofdata tuples; monitoring each of the plurality of data tuples processedby the plurality of operators in the operator graph to determine timefor the plurality of operators to process each of the plurality of datatuples and a number of times each of the plurality of operators processeach of the plurality of data tuples; defining a tuple breakpoint thatfires when at least one of the plurality of data tuples has beenprocessed more than a specified limit for processing the at least onedata tuple by all of the plurality of operators that process the atleast one data tuple; and when the tuple breakpoint fires, halting atleast one operator in the operator graph and displaying informationregarding a data tuple that caused the tuple breakpoint to fire.
 11. Themethod of claim 10 wherein the specified limit comprises a time limitfor processing the at least one data tuple.
 12. The method of claim 10wherein the specified limit comprises a number of times for at least twoof the plurality of operators in the operator graph to process the atleast one data tuple.
 13. The method of claim 10 wherein the specifiedlimit comprises a number of times for an individual operator in theoperator graph to process the at least one data tuple.
 14. The method ofclaim 10 wherein the tuple breakpoint mechanism halts a defined list ofoperators in the operator graph when the tuple breakpoint fires.
 15. Themethod of claim 10 wherein the tuple breakpoint mechanism halts anoperator in the operator graph that caused the tuple breakpoint to fire.16. The method of claim 10 wherein the tuple breakpoint mechanism haltsan operator in the operator graph that used the most amount of timeprocessing the data tuple that caused the tuple breakpoint to fire. 17.The method of claim 10 wherein the tuple breakpoint mechanism halts anoperator in the operator graph that processed the most times the datatuple that caused the tuple breakpoint to fire.
 18. The method of claim10 wherein the tuple breakpoint mechanism displays information regardingthe data tuple that caused the tuple breakpoint to fire wherein thedisplayed information comprises at least one of: when the data tuplethat caused the tuple breakpoint to fire was created; time the datatuple that caused the tuple breakpoint to fire was in the operatorgraph; number of operators that processed the data tuple that caused thetuple breakpoint to fire; list of operators that processed the datatuple that caused the tuple breakpoint to fire; and how many times eachoperator in the list processed the data tuple that caused the tuplebreakpoint to fire.
 19. A computer-implemented method executed by atleast one processor for debugging a streaming application, the methodcomprising: executing a streaming application that comprises an operatorgraph that includes a plurality of operators that process a plurality ofdata tuples; monitoring each of the plurality of data tuples processedby the plurality of operators in the operator graph to determine timefor the plurality of operators to process each of the plurality of datatuples and a number of times each of the plurality of operators processeach of the plurality of data tuples; defining a tuple breakpoint thatfires when at least one of the plurality of data tuples has beenprocessed more than a specified limit for processing the at least onedata tuple by all of the plurality of operators that process the atleast one data tuple when at least one of the following is satisfied: atime limit for processing the at least one data tuple; a number of timesfor at least two of the plurality of operators in the operator graph toprocess the at least one data tuple; and a number of times for anindividual operator in the operator graph to process the at least onedata tuple; when the tuple breakpoint fires, halting at least one of:all operators in the operator graph; a defined list of operators in theoperator graph; an operator in the operator graph that caused the tuplebreakpoint to fire; an operator in the operator graph that used the mostamount of time processing the tuple that caused the tuple breakpoint tofire; and an operator in the operator graph that processed the tuplethat caused the tuple breakpoint to fire the most times; displayinginformation regarding a tuple that caused the tuple breakpoint to firewherein the displayed information comprises: when the data tuple thatcaused the tuple breakpoint to fire was created; time the data tuplethat caused the tuple breakpoint to fire was in the operator graph;number of operators that processed the data tuple that caused the tuplebreakpoint to fire; list of operators that processed the data tuple thatcaused the tuple breakpoint to fire; and how many times each operator inthe list processed the data tuple that caused the tuple breakpoint tofire.
 20. A computer-implemented method executed by at least oneprocessor for debugging a streaming application, the method comprising:executing a streaming application that comprises an operator graph thatincludes a plurality of operators that process a plurality of datatuples; monitoring each of the plurality of data tuples processed by theplurality of operators in the operator graph to determine time for theplurality of operators to process each of the plurality of data tuplesand a number of times each of the plurality of operators process each ofthe plurality of data tuples; defining a tuple breakpoint that fireswhen at least one of the plurality of data tuples has been-processedmore than a specified limit for processing the at least one data tupleby all of the plurality of operators that process the at least one datatuple; and when the tuple breakpoint fires, halting an operator in theoperator graph that processed the most times the data tuple that causedthe tuple breakpoint to fire without halting all of the plurality ofoperators; and displaying information regarding a tuple that caused thetuple breakpoint to fire, wherein the displayed information compriseswhen the data tuple that caused the tuple breakpoint to fire wascreated.
 21. The method of claim 20 wherein the displayed informationfurther comprises time the data tuple that caused the tuple breakpointto fire was in the operator graph.
 22. The method of claim 21 whereinthe displayed information further comprises a number of operators thatprocessed the data tuple that caused the tuple breakpoint to fire. 23.The method of claim 22 wherein the displayed information furthercomprises a list of operators that processed the data tuple that causedthe tuple breakpoint to fire.
 24. The method of claim 23 wherein thedisplayed information further comprises how many times each operator inthe list processed the data tuple that caused the tuple breakpoint tofire.